Active Learning Methods for Remote Sensing Image Classification
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 26124
Special Issue Editors
Interests: sustainable cities and community development
Special Issues, Collections and Topics in MDPI journals
Interests: image classification; spatial analysis; deep learning; sample learning; urban landscape
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; pattern recognition; image processing; machine learning; deep learning; object detection and tracking; video analysis; remote sensing applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing image classification (RSIC) plays a fundamental role in large-scale land resource surveys, ecological environment assessment, and human group behavior monitoring. Although past decades have witnessed great improvement in RSIC methods, especially for using deep learning neural networks, RSIC has encountered a bottleneck in that it requires huge samples and has poor accuracy in scenes where samples are scarce. Active learning serves as a possible solution for the issue, as it needs limited samples and can be adaptive to variant scenes. Accordingly, the progress of active learning methods will facilitate the development of RSIC.
We would like to invite you to contribute to this Special Issue on “Active Learning Methods for Remote Sensing Image Classification” which will gather insights and contributions to the field of active learning for RSIC. In the Special Issue, original research articles, reviews, and novel remote sensing data sets are welcome. Papers can be focused on but are not limited to:
- Supervised/ unsupervised/ semi-supervised/ reinforcement/ transfer learning methods for RSIC;
- Sample extraction, enhancement, and transformation for RSIC;
- Deep learning frameworks and methods for RSIC;
- Remote sensing data sets and benchmark for RSIC.
Dr. Xiuyuan Zhang
Prof. Dr. Shihong Du
Prof. Dr. Gong Cheng
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- Active learning
- Transfer learning
- Reinforcement learning
- Deep learning
- Image classification
- Land cover/land use mapping
- Samples
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